Bibliographic record
Abstract
accounting explanation, wealth inequality 178-85 ADS survey 152-4 African Americans portfolio span 369 wealth 64-80, 82-3 age and portfolio composition, US 369, 380-82, 423 and portfolio span, US 369 and stock ownership, US 141 and wealth Canada 154, 168, 175 Germany 205-11, 216 Sweden 281, 283-4, 288-91 US 91-4, 129-33 and wealth gap, Canada 175 and wealth polarization, US 416, 432 ageing population and wealth inequality, Canada 171 Altonji, J. 175 Andersson, B. 286, 290 Ando, A. 226 asset ownership African Americans 73 Chile 332-7 parental influences 344-8, 351-4 US 92-3, 101-2, 122-4, 134 Atkinson, A.B. 261-2 average wealth Canada 154-6 US 145 baby boomers retirement wealth, Sweden 288-91, 292 wealth, US 59-64, 82 Bank of Italy, household wealth survey (SHIW) 230-41 Becker, G. 330 black people, US, see African Americans Blank, R.M. 394 Blau, F. 173 Börsch-Supan, A. 195 Burkhauser, R.V. 195, 217, 218, 394 business equity ownership Chile 334 and wealth inequality, Canada 183 businesses valuation, SHIW 231-2 Canada family structure and wealth inequality 169-72 wealth data 154-6 wealth inequality 156-87 wealth surveys 152-3 Cannari, L. 237, 272 Card, D. 394 children effect on household wealth, US 131-3 parental wealth and living standards, Chile 337-60 effect on stock ownership, US 142-3 Chile asset ownership 332-7 parental wealth and children's outcomes 337-60 parental wealth and home ownership 354-9 composition of household wealth, see portfolio composition consumer durables valuation, SHIW 231 consumption behaviour, effects of parental wealth, Chile 341-4 Cowell, F.A. 256
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.547 | 0.503 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".